MCOO-SLAM: A Multi-Camera Omnidirectional Object SLAM System

TL;DR

MCOO-SLAM introduces a multi-camera omnidirectional SLAM system that enhances object mapping and localization in outdoor environments by leveraging surround-view cameras, semantic fusion, and scene graph abstraction.

Contribution

It presents a novel multi-camera system with semantic and geometric fusion, robust object association, and a hierarchical scene graph for improved outdoor SLAM performance.

Findings

Achieves accurate localization in complex outdoor scenarios.

Provides scalable, robust object-level mapping with occlusion handling.

Enables viewpoint-invariant place recognition through omnidirectional loop closure.

Abstract

Object-level SLAM offers structured and semantically meaningful environment representations, making it more interpretable and suitable for high-level robotic tasks. However, most existing approaches rely on RGB-D sensors or monocular views, which suffer from narrow fields of view, occlusion sensitivity, and limited depth perception-especially in large-scale or outdoor environments. These limitations often restrict the system to observing only partial views of objects from limited perspectives, leading to inaccurate object modeling and unreliable data association. In this work, we propose MCOO-SLAM, a novel Multi-Camera Omnidirectional Object SLAM system that fully leverages surround-view camera configurations to achieve robust, consistent, and semantically enriched mapping in complex outdoor scenarios. Our approach integrates point features and object-level landmarks enhanced with open-vocabulary semantics. A semantic-geometric-temporal fusion strategy is introduced for robust object association across multiple views, leading to improved consistency and accurate object modeling, and an omnidirectional loop closure module is designed to enable viewpoint-invariant place recognition using scene-level descriptors. Furthermore, the constructed map is abstracted into a hierarchical 3D scene graph to support downstream reasoning tasks. Extensive experiments in real-world demonstrate that MCOO-SLAM achieves accurate localization and scalable object-level mapping with improved robustness to occlusion, pose variation, and environmental complexity.